Search

Over the last decade, the electricity sector has seen a significant increase in the number of residential battery systems, as well as increasing interest in using them to reduce demand during network peaks. Although there is an abundance of literature assessing this ability using modelled residential batteries, there is a lack of detailed assessment using deployed residential batteries. This paper analyses 1-min resolution data from 15 non-coordinated residential batteries deployed in Australia across 6 network peak demand periods. A novel metric was used to quantify errors in BESS load-following, which occurred when the batteries did not completely mitigate grid import and export even when they had sufficient energy capacity and rated power. On average the 15 batteries discharged around 25% of their rated power during network demand peaks, whereas those that load-followed discharged around 40%. Despite the small sample size, these results suggest that the outcomes from modelled batteries represent the ideal upper bound and the actual performance of some batteries is likely to be lower. This there is a need for more research into the actual operation of deployed batteries, and what this means for the current modelled findings regarding their ability to reduce demand during network peaks. • Analysis of operation of residential batteries (BESS) was performed using 1-min data. • Examined 90 cases of response from uncoordinated BESS during demand peaks. • In 44 cases, the uncoordinated BESS reduced household grid import on average by 90%. • Those BESS discharged on average 40%–50% of their rated power during the peaks. • Coordinating residential BESS may potentially double their demand peak reduction. [ABSTRACT FROM AUTHOR]

The penetration of distributed battery energy storage systems in electricity grids globally is forecast to grow rapidly over the next 10 years. These systems can provide benefits for the customers that install them, but also introduce new opportunities and challenges for distribution network operators. This paper presents an algorithm that optimises the sizing and operation of a cluster of battery energy storage systems in commercial buildings. It aims to either optimise the battery energy storage system independently to maximise the financial benefits for each building or coordinate them to maximise the value of reducing demand peaks for both the customer and the local zone substation. To achieve the latter, the algorithm assumes a certain percentage of the network deferment value associated with reducing the peak demand for the zone substation is passed through to customers. Scenario analysis show that the peak demand reduction and financial outcomes for both the building owners and the network result from a complex interaction between the customer and network load profiles, the tariff structure, the size and cost of the BESS, and the percentage of the network deferment value that is passed through. • Optimised battery size and operation for a cluster of commercial building. • Optimisation model included a deferment value (NDV) for reducing substation peaks. • Results showed that the NDV was effective in encouraging larger battery sizes. • Best outcome for network operators is when half of the NDV is shared with customers. • Not including the NDV can still result in beneficial outcomes for customers. [ABSTRACT FROM AUTHOR]